Obstacle distribution simulation method and device based on multiple models, and storage medium

ABSTRACT

An obstacle distribution simulation method, device and terminal based on multiple models. The method can include: acquiring a point cloud, the point cloud including a plurality of obstacles labeled with real labeling data; extracting the real labeling data of the obstacles, and training a plurality of neural network models based on the real labeling data of the obstacles; extracting unlabeled data in the point cloud, inputting the unlabeled data into the neural network models, and outputting a plurality of prediction results. The plurality of prediction results can include a plurality of simulated obstacles with attribute data; selecting at least one simulated obstacle based on the plurality of prediction results; and inputting the attribute data of the selected simulated obstacle into the neural network models to obtain position coordinates of the simulated obstacle, and further obtain a position distribution of the simulated obstacle.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.201811044615.9, filed on Sep. 7, 2018, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to obstacle detection, and inparticular to an obstacle distribution simulation method and devicebased on multiple models, and a computer readable storage mediumtherefor.

BACKGROUND

High-precision three-dimensional (3D) maps can be annotated with datalabeling obstacles in the map. This data can be collected in an offlinestate of the high-precision map. This labeling, or annotation, data caninclude a position indicating where an obstacle is currently located, anorientation, an identifier (ID) a type of the obstacle, and the like.The obstacle types can include dynamic obstacles such as a vehicles,pedestrians or bicycle riders, and static obstacles such as trafficcones or pylons. The question of how to simulate or model the number ofobstacles and the position distribution of the obstacles so as toresemble the real conditions as much as possible is drawing more andmore attention from those skilled in the art.

In existing technical solutions, simulation is typically performed byusing a regular (or rule-based) arrangement of obstacles with the aid ofa high-precision map. Examples of regular arrangement of obstacles mightinclude arrangements of vehicles in a generally parallel to a lane, andrandom arrangements of pedestrians. However, very limited scenarios canbe presented based on regular arrangement of obstacles. Since thehigh-precision maps often include only main roads, without side roads orbranch roads, a simulated result of the position distribution of thesame types of obstacles and a simulated result of the numberdistribution of different types of obstacles can greatly differ fromreal conditions. In addition, the regular arrangement of obstaclescannot present all the possible cases in a real scenario in anexhaustive manner, resulting in low coverage.

SUMMARY OF THE DISCLOSURE

According to embodiments of the present disclosure, a multiple-modelbased obstacle distribution simulation method, device and terminal areprovided, to solve at least the above technical problems in the existingtechnologies.

In a first aspect, an obstacle distribution simulation method based onmultiple models can include acquiring a point cloud including aplurality of obstacles labeled with real labeling data. The point cloudcan be based on a plurality of imaging frames. The real labeling data ofthe obstacles can be extract, and a plurality of neural network modelscan be trained based on the real labeling data of the obstacles.

Unlabeled data in the point cloud can be extracted and input into theneural network models. A plurality of prediction results, which caninclude a plurality of simulated obstacles with attribute data can beoutput.

At least one simulated obstacle can be selected based on the pluralityof prediction results. The attribute data of the selected simulatedobstacle can be input into the plurality of neural network models toobtain position coordinates of the simulated obstacle, and furtherobtain a position distribution of the simulated obstacle.

In embodiments, the method can further include outputting confidencescorresponding to the respective prediction results determining whetherthe confidences are greater than a threshold, and reserving, retaining,or outputting a the prediction results that have a confidence greaterthan the threshold.

Selecting at least one simulated obstacle based on the plurality ofprediction results can include determining whether the plurality ofprediction results include a common same simulated obstacle, andselecting the same simulated obstacle in a case that the plurality ofprediction results include the same simulated obstacle.

In embodiments, inputting the attribute data of the selected simulatedobstacle into the neural network models to obtain position coordinatesof the simulated obstacle can include inputting the attribute data ofthe selected simulated obstacle into the neural network models to obtaina plurality of boundary boxes of the simulation obstacle, obtaininglengths and widths of the boundary boxes of the simulated obstacles,calculating an average length value and an average width value based onthe lengths and the widths of the boundary boxes, and obtaining centercoordinates of an average boundary box based on the average length valueand the average width value to cause the central coordinates to berepresented as position coordinates of the simulated obstacle.

In a second aspect, according to an embodiment of the presentdisclosure, an obstacle distribution simulation device based on multiplemodels is provided. The device can include a point cloud acquisitionmodule, configured to acquire a point cloud based on a plurality offrames, the point cloud including a plurality of obstacles labeled byreal labeling data. A model training module can be configured to extractthe real labeling data of the obstacles, and train a plurality of neuralnetwork models based on the real labeling data of the obstacles. Asimulated obstacle prediction module can be configured to extractunlabeled data in the point cloud, input the unlabeled data into theneural network models, and output a plurality of prediction results,wherein the plurality of prediction results include a plurality ofsimulated obstacles with attribute data. A simulated obstacle selectionmodule can be configured to select at least one simulated obstacle basedon the plurality of prediction results. A simulated obstacle positiondistribution module can be configured to input the attribute data of theselected simulated obstacle into the neural network models to obtainposition coordinates of the simulated obstacle, and further obtain aposition distribution of the simulated obstacles.

In embodiments, the device can further include a confidencedetermination module configured to output confidences corresponding tothe respective prediction results, determine whether the confidences aregreater than a threshold, and reserve or retain a prediction result witha confidence greater than the threshold.

In embodiments, the simulated obstacle position distribution module caninclude, a boundary box calculation unit, configured to input theattribute data of the selected simulated obstacle into the neuralnetwork models to obtain a plurality of boundary boxes of the simulationobstacle, a length and width calculation unit, configured to obtainlengths and widths of the boundary boxes of the simulation obstacle, anaverage length and width value calculation unit, configured to calculatean average length value and an average width value based on the lengthsand the widths of the boundary boxes; and a position coordinatecalculation unit, configured to obtain center coordinates of an averageboundary box based on the average length value and the average widthvalue, to cause the center coordinates to be represented as positioncoordinates of the simulated obstacle.

In a third aspect, according to an embodiment of the present disclosure,an obstacle distribution simulation terminal based on multiple models isprovided. The terminal can include: a processor and a memory for storinga program which supports the obstacle distribution simulation devicebased on multiple models in executing the obstacle distributionsimulation method based on multiple models described above in the firstaspect, and the processor is configured to execute the program stored inthe memory. The terminal can further include a communication interfacefor enabling the terminal to communicate with other devices orcommunication networks.

The functions may be implemented by using hardware or by executingcorresponding software by hardware. The hardware or software includesone or more modules corresponding to the functions described above.

In a fourth aspect, according to an embodiment of the presentdisclosure, a non-transitory, computer-readable storage medium isprovided for storing computer software instructions used for an obstacledistribution simulation device based on multiple models, the computerreadable storage medium including a program involved in executing theobstacle distribution simulation method based on multiple modelsdescribed above in the first aspect by the obstacle distributionsimulation device based on multiple models.

Any one of the above technical solutions can have the followingadvantages or advantageous effects. By extracting the real labeling dataof the obstacles to training the neural network models, simulatedobstacles can be predicted using the unlabeled data and the neuralnetwork models, and a position distribution of the predicted simulatedobstacles can be calculated. The position diversity of the simulatedobstacles is increased, such that simulation results of the positiondistribution of the obstacles and the number distribution of theobstacles are closer to real conditions.

The above summary is provided only for illustration, and is not intendedto limit the present disclosure in any way. In addition to theillustrative aspects, embodiments and features described above, furtheraspects, embodiments and features of the present disclosure may bereadily understood from the following detailed description withreference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Unless otherwise specified, identical or similar parts or elements aredenoted by identical reference signs throughout several figures of theaccompanying drawings. The drawings are not necessarily drawn to scale.It should be understood that these drawings merely illustrate someembodiments of the present disclosure, and should not be construed aslimiting the scope of the disclosure.

FIG. 1 is a flowchart depicting an obstacle distribution simulationmethod based on multiple models according to an embodiment of thepresent disclosure;

FIG. 2 is a schematic diagram depicting a boundary box of a simulatedobstacle according to an embodiment of the present disclosure;

FIG. 3 is a block diagram depicting the structure of an obstacledistribution simulation device according to an embodiment of the presentdisclosure;

FIG. 4 is a block diagram depicting the structure of a simulatedobstacle position distribution module according to an embodiment of thepresent disclosure; and

FIG. 5 is a schematic diagram of an obstacle distribution simulationterminal based on multiple models according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Hereinafter, only some example embodiments are described. As can beappreciated by those skilled in the art, the described embodiments maybe modified in various different ways without departing from the spiritor scope of the present disclosure. Accordingly, the drawings and thedescription should be considered as illustrative in nature instead ofbeing restrictive.

FIG. 1 is a flowchart depicting an obstacle distribution simulationmethod based on multiple models. At S100, a point cloud based on aplurality of frames can be acquired, the point cloud can include aplurality of obstacles labeled by real labeling data.

When an acquisition vehicle moves according to a movement rule, theacquisition vehicle may obtain a point cloud based on a plurality offrames, by scanning the surrounding obstacles using radar, or othersensing techniques. The acquisition vehicle may move along a main roador along a specified side road, and various movements of the acquisitionvehicle will fall within the scope of embodiments of the presentdisclosure. The point cloud may also be obtained directly from theoutside.

At S200, the real labeling data of the obstacles can be extracted, and aplurality of neural network models can be trained based on the reallabeling data of the obstacles.

The obstacles in each frame of the point cloud can be labeled based onabsolute coordinates of the obstacles, such that each obstacle hascorresponding real labeling data. Specifically, in embodiments, theabsolute coordinates of the acquisition vehicle in the world coordinatesystem can be acquired. In each frame of the point cloud, a point cloudcoordinate system can be established by taking the acquisition vehicleas the origin, and the relative coordinates of each obstacle withrespect to the acquisition vehicle can be calculated. Based on theabsolute coordinates of the acquisition vehicle and the relativecoordinates of the obstacle, the absolute coordinates of the obstacle inthe world coordinate system can be obtained. The obstacle can be labeledbased on the absolute coordinates of the obstacle such that the reallabeling data of the obstacle includes the absolute coordinates of theobstacle. Multiple neural network models can be designed based on thereal labeling data of the obstacles. The multiple neural network modelscan have the same function, but may have different network structuresand different parameters. Common network structures can include VGGnet(Visual Geometry Group), ResNet (Deep residual network) GoogleNet, andthe like. The parameters can correspond to the network structures.

At S300, unlabeled data in the point cloud can be extracted, theunlabeled data can be input into the neural network models, and aplurality of prediction results can be output. The plurality ofprediction results can include a plurality of simulated obstacles withattribute data.

Not all the obstacles are labeled in each frame of the point cloud, andthe unlabeled obstacles each have absolute coordinates, which constituteunlabeled data in the point cloud. The unlabeled data can be input intomultiple neural network models to predict the simulated obstacles. Theattribute data of each of the simulated obstacles can include anidentity recognition ID, a type, an orientation of the simulatedobstacle, and on the like. It should be noted that the predictionresults obtained from different neural network models may be different.That is, the number, the type, and so on of the simulated obstacles maybe different in different prediction results.

At S400, at least one simulated obstacle can be selected based on theplurality of prediction results.

At S500, the attribute data of the selected simulated obstacle can beinput into the neural network models to obtain position coordinates ofthe simulated obstacle, and to further obtain a position distribution ofthe simulated obstacle.

As a result of executing the methods of embodiments of the presentdisclosure, the position diversity of the simulated obstacles can beincreased, such that a simulated result of the position distribution ofthe simulated obstacles and a simulated result of the numberdistribution of the simulated obstacles are closer to real conditions.

In an embodiment, after outputting a plurality of prediction results,the method can further includes outputting confidences corresponding tothe respective prediction results, determining whether the confidencesare greater than a threshold, and retaining prediction results withconfidences greater than the threshold.

The confidence for each prediction result can be output by the neuralnetwork model. The confidence can be used to indicate reliability of theprediction result. The confidence corresponding to the prediction resultis determined, a prediction result with a confidence greater than thethreshold can be retained, and the prediction result having a confidenceless than the threshold can be discarded.

In an embodiment, selecting at least one simulated obstacle based on theplurality of prediction results can include determining whether theplurality of prediction results includes a common simulated obstacle,and selecting the common simulated obstacle when the plurality ofprediction results includes the common simulated obstacle.

By way of example, the neural network models might include a firstmodel, a second model and a third model. The unlabeled data can be inputinto each of the first model, the second model, and the third model toobtain attribute A data, attribute B data, and attribute C datarespectively. If the attribute A data, attribute B data, and attribute Cdata all indicate the same obstacle X, the obstacle X is selected as asimulated obstacle. Alternatively, if the number of prediction resultswhich indicate the same obstacle is greater than or equal to a thresholdnumber, the obstacle is selected as the simulated obstacle. For example,if both the attribute A data and the attribute B data indicate anotherobstacle Y, and the threshold number is 2, then the obstacle Y isselected as a simulated obstacle.

In an embodiment, obtaining the position coordinates of each of thesimulated obstacles according to the neutral network models can includeinputting the attribute data of the selected simulated obstacle into theneural network models to obtain position coordinates of the simulatedobstacle. This can include inputting the attribute data of the selectedsimulated obstacle into the neural network models to obtain a pluralityof boundary boxes of the simulated obstacle, obtaining lengths andwidths of the boundary boxes of the simulated obstacle, calculating anaverage length value and an average width value based on the lengths andwidths of the boundary boxes. Center coordinates of an average boundarybox can be determined based on the average length value and the averagewidth value, to cause the center coordinates to be represented asposition coordinates of the simulated obstacle.

As shown in FIG. 2, a “STOP” simulated obstacle is provided as anillustrative example. A first boundary box and a second boundary box forthe “STOP” simulated obstacle are acquired by calculation of two neuralnetwork models. The position coordinates of the simulated obstacle arecalculated using the first boundary box and the second boundary box.

FIG. 3 is a schematic diagram depicting an obstacle distributionsimulation device based on multiple models according to an embodiment. Apoint cloud acquisition module 10 can be configured to acquire a pointcloud based on a plurality of frames. The point cloud can include aplurality of obstacles labeled with real labeling data.

A model training module 20 can be configured to extract the reallabeling data of the obstacles, and to train a plurality of neuralnetwork models based on the real labeling data of the obstacles.

A simulated obstacle prediction module 30 can be configured to extractunlabeled data in the point cloud, input the unlabeled data into theneural network models, and output a plurality of prediction results. Theplurality of prediction results can include a plurality of simulatedobstacles with attribute data.

A simulated obstacle selection module 40 can be configured to select atleast one simulated obstacle based on the plurality of predictionresults.

A simulated obstacle position distribution module 50 can be configuredto input the attribute data of the selected simulated obstacle into theneural network models to obtain position coordinates of the simulatedobstacle, and further obtain a position distribution of the simulatedobstacle.

In embodiments, the device can further include a confidencedetermination module (not shown). The confidence determination modulecan be configured to output confidences corresponding to the respectiveprediction results, determine whether the confidences are greater than athreshold, and retain prediction results with confidences greater thanthe threshold.

In embodiments, as shown in FIG. 4, the simulated obstacle positiondistribution module 50 can include a number of sub units. A boundary boxcalculation unit 51 can be configured to input the attribute data of theselected simulated obstacle into the neural network models to obtain aplurality of boundary boxes of the simulation obstacle. A length andwidth calculation unit 52 can be configured to obtain lengths and widthsof the boundary boxes of the simulation obstacle. An average length andwidth value calculation unit 53 can be configured to calculate anaverage length value and an average width value based on the lengths andwidths of the boundary boxes. A position coordinate calculation unit 54can be configured to obtain center coordinates of an average boundarybox based on the average length value and the average width value, tocause the center coordinates to be represented as position coordinatesof the simulated obstacles.

FIG. 5 is a schematic diagram depicting a terminal for obstacledistribution simulation based on multiple models is provided accordingto an embodiment of the present disclosure.

The terminal can include a memory 400 and a processor 500, wherein acomputer program that can run on the processor 500 is stored in thememory 400. When the processor 500 executes the computer program, theobstacle distribution simulation method based on multiple modelsaccording to the above embodiment is implemented. There may be one ormore of each of memory 400 and processor 500 in the terminal.

A communication interface 600 can be configured to enable the memory 400and the processor 500 to communicate with an external device.

The memory 400 may include a high-speed RAM memory, or may also includea non-volatile memory, such as at least one disk memory.

If the memory 400, the processor 500 and the communication interface 600are implemented independently, the memory 400, the processor 500 and thecommunication interface 600 may be connected to each other via a bus soas to realize mutual communication. The bus may be an industry standardarchitecture (ISA) bus, a peripheral component interconnect (PCI) bus,an extended industry standard architecture (EISA) bus, or the like. Thebus may be categorized into an address bus, a data bus, a control bus,or the like. For ease of illustration, only one bold line is shown inFIG. 5 to represent the bus, but it does not mean that there is only onebus or only one type of bus.

Optionally, in embodiments, if the memory 400, the processor 500 and thecommunication interface 600 are integrated on one chip, then the memory400, the processor 500 and the communication interface 600 can completemutual communication through an internal interface.

An embodiment of the present disclosure provides a non-transitorycomputer readable storage medium having a computer program storedthereon which, when executed by a processor, implements the obstacledistribution simulation method based on multiple models described in anyof the above embodiments.

In the present specification, the description referring to the terms“one embodiment”, “some embodiments”, “an example”, “a specificexample”, or “some examples” or the like means that the specificfeatures, structures, materials, or characteristics described inconnection with the embodiment or example are contained in at least oneembodiment or example of the present disclosure. Moreover, the specificfeatures, structures, materials, or characteristics described may becombined in a suitable manner in any one or more of the embodiments orexamples. In addition, various embodiments or examples described in thespecification as well as features of different embodiments or examplesmay be united and combined by those skilled in the art, as long as theydo not contradict with each other.

Furthermore, terms “first” and “second” are used for descriptivepurposes only, and are not to be construed as indicating or implyingrelative importance or implicitly indicating the number of recitedtechnical features. Thus, a feature defined with “first” and “second”may include at least one said feature, either explicitly or implicitly.In the description of the present disclosure, the meaning of “aplurality” is two or more than two, unless otherwise explicitly orspecifically indicated.

Any process or method described in the flowcharts or described otherwiseherein may be construed as representing a module, segment or portionincluding codes for executing one or more executable instructions forimplementing particular logical functions or process steps. The scope ofthe preferred embodiments of the present disclosure includes additionalimplementations in which functions may be implemented in an order thatis not shown or discussed, including in a substantially concurrentmanner or in a reverse order based on the functions involved. All theseshould be understood by those skilled in the art to which theembodiments of the present disclosure belong.

The logics and/or activities represented in the flowcharts or otherwisedescribed herein for example may be considered to be an ordered list ofexecutable instructions for implementing logical functions. They can bespecifically embodied in any computer readable medium for use by aninstruction execution system, apparatus or device (e.g., acomputer-based system, a system including a processor, or another systemthat can obtain instructions from the instruction execution system,apparatus or device and execute these instructions) or for use inconjunction with the instruction execution system, apparatus or device.For the purposes of the present specification, “computer readablemedium” can be any means that can contain, store, communicate, propagateor transmit programs for use by an instruction execution system,apparatus or device or for use in conjunction with the instructionexecution system, apparatus or device. More specific examples(non-exhaustive list) of computer readable storage medium at leastinclude: electrical connection parts (electronic devices) having one ormore wires, portable computer disk cartridges (magnetic devices), randomaccess memory (RAM), read only memory (ROM), erasable programmableread-only memory (EPROM or flash memory), fiber optic devices, andportable read only memory (CDROM). In addition, the computer-readablestorage medium may even be a paper or other suitable medium on which theprograms can be printed. This is because for example the paper or othermedium can be optically scanned, followed by editing, interpretation or,if necessary, other suitable ways of processing so as to obtain theprograms electronically, which are then stored in a computer memory.

It should be understood that individual portions of the presentdisclosure may be implemented in the form of hardware, software,firmware, or a combination thereof. In the above embodiments, aplurality of steps or methods may be implemented using software orfirmware stored in a memory and executed by a suitable instructionexecution system. For example, if they are implemented in hardware, asin another embodiment, any one or a combination of the followingtechniques known in the art may be used: discrete logic circuits havinglogic gate circuits for implementing logic functions on data signals,application-specific integrated circuits having suitable combined logicgate circuits, programmable gate arrays (PGA), field programmable gatearrays (FPGA), etc.

Those skilled in the art may understand that all or part of the stepscarried in the method of the foregoing embodiments may be implemented byusing a program to instruct the relevant hardware, and the program maybe stored in a computer readable storage medium. When executed, theprogram includes one or a combination of the steps in the methodembodiments.

In addition, individual functional units in various embodiments of thepresent disclosure may be integrated in one processing module, orindividual units may also exist physically and independently, or two ormore units may also be integrated in one module. The above integratedmodule can be implemented in the form of hardware or in the form of asoftware functional module. The integrated module may also be stored ina computer readable storage medium if it is implemented in the form of asoftware function module and sold or used as a stand-alone product. Thestorage medium may be a read-only memory, a magnetic disk or an opticaldisk, etc.

The above description only relates to specific embodiments of thepresent disclosure, but the scope of protection of the presentdisclosure is not limited thereto, and any of those skilled in the artcan readily contemplate various changes or replacements within thetechnical scope of the present disclosure. All these changes orreplacements should be covered by the scope of protection of the presentdisclosure. Therefore, the scope of protection of the present disclosureshould be determined by the scope of the appended claims.

What is claimed is:
 1. An obstacle distribution simulation method basedon multiple models, comprising: acquiring a point cloud based on aplurality of frames, the point cloud comprising a plurality of obstacleslabeled with real labeling data; extracting the real labeling data ofthe plurality of obstacles, and training a plurality of neural networkmodels based on the real labeling data of the plurality of obstacles;extracting unlabeled data of unlabeled obstacles from the point cloud,inputting the unlabeled data into the plurality of neural networkmodels, and outputting, for the unlabeled data, a plurality ofprediction results obtained by the plurality of neural network models,wherein the plurality of prediction results comprise a plurality ofsimulated obstacles with attribute data; selecting a simulated obstaclebased on the plurality of prediction results; and inputting theattribute data of the selected simulated obstacle into the plurality ofneural network models to obtain position coordinates of the simulatedobstacle using the plurality of neural network models, and furtherobtain a position distribution of the simulated obstacle using theplurality of neural network models.
 2. The method of claim 1, furthercomprising: outputting confidences corresponding to the respectiveprediction results; and determining whether the confidences are greaterthan a threshold, and reserving a prediction result with a confidencegreater than the threshold.
 3. The method of claim 1, wherein selectingat least one simulated obstacle based on the plurality of predictionresults comprises: determining whether the plurality of predictionresults comprise a common simulated obstacle, and selecting the commonsimulated obstacle when the plurality of prediction results comprise thecommon simulated obstacle.
 4. The method of claim 1, wherein inputtingthe attribute data of the selected simulated obstacle into the pluralityof neural network models to obtain position coordinates of the simulatedobstacle comprises: inputting the attribute data of the selectedsimulated obstacle into the plurality of neural network models to obtaina plurality of boundary boxes of the simulated obstacle; obtaining alength and a width of the each of the plurality of boundary boxes of thesimulated obstacle; calculating an average length value and an averagewidth value based on the lengths and widths of the plurality of boundaryboxes; and calculating center coordinates of an average boundary boxbased on the average length value and the average width value such thatthe center coordinates are represented as position coordinates of thesimulated obstacle.
 5. An obstacle distribution simulation device basedon multiple models, the device comprising: one or more processors; and astorage device configured to store one or more programs, that, whenexecuted by the one or more processors, cause the one or more processorsto: acquire a point cloud based on a plurality of frames, the pointcloud comprising a plurality of obstacles labeled by real labeling data;extract the real labeling data of the plurality of obstacles, and traina plurality of neural network models based on the real labeling data ofthe plurality of obstacles; extract unlabeled data of unlabeledobstacles from the point cloud, input the unlabeled data into theplurality of neural network models, and output, for the unlabeled data,a plurality of prediction results obtained by the plurality of neuralnetwork models, wherein the plurality of prediction results comprise aplurality of simulated obstacles with attribute data; select a simulatedobstacle based on the plurality of prediction results; and input theattribute data of the selected simulated obstacle into the plurality ofneural network models to obtain position coordinates of the simulatedobstacle using the plurality of neural network models, and to furtherobtain a position distribution of the simulated obstacle using theplurality of neural network models.
 6. A non-transitory computerreadable storage medium, in which a computer program is stored, whereinthe program, when executed by a processor, causes the processor toimplement the method of claim
 1. 7. The device of claim 5, wherein theone or more programs, when executed by the one or more processors, causethe one or more processors further to: output a confidence correspondingto the each of the plurality of prediction results; determine whethereach confidence is greater than a threshold; and retain each predictionresult having a confidence greater than the threshold.
 8. The device ofclaim 5, wherein the one or more programs, when executed by the one ormore processors, cause the one or more processors further to: input theattribute data of the selected simulated obstacle into the plurality ofneural network models to obtain a plurality of boundary boxes of thesimulated obstacle; obtain lengths and widths of the plurality ofboundary boxes of the simulated obstacle; calculate an average lengthvalue and an average width value based on the lengths and widths of theplurality of boundary boxes; and calculate center coordinates of anaverage boundary box based on the average length value and the averagewidth value, such that the center coordinates are represented asposition coordinates of the simulated obstacle.